Estimation of the head sensitivity function in scanning magnetoresistance microscopy

We apply Bayesian image analysis techniques to a problem in a newly develop ed scanned probe technology that uses commercial magnetoresistive (MR) reco rd and playback heads as probes to sense magnetic fields. This technology c an be used for magnetic imaging and for evaluating playback and record proc esses in magnetic recording. In MR microscopy, an MR head is raster scanned while in physical contact with a magnetic sample (e.g., hard disk media, t ape, or fine magnetic particles). By plotting the MR resistance as a functi on of position, a very high resolution (on the order of. 1 x 1.0 mum) magne tic image of the sample is constructed. This case study focuses on characte rizing the head sensitivity function (HSF), which depends on the physical d imensions and the magnetic properties of the MR head. These sensitivity fun ctions are of great practical interest because they ultimately relate to th e head's performance in a high-density data storage environment. Estimating the HSF requires a deconvolution that has features that prevent the proble m from being straightforward: both the HSF and the source being scanned are unknown, and there is a substantial amount of correlated noise in the scan ned image. We take a Bayesian approach to model and estimate the HSF, while accounting for noise and other nuisance effects such as thermal drift. Bes ides yielding a point estimate, which is a fairly difficult task here, this approach also quantifies uncertainty so we can assess whether certain feat ures of the estimated head sensitivity function appear to be genuine.